论文标题
神经网络，用于估计光学和浊度散射介质的光学特征
Neural network for estimation of optical characteristics of optically active and turbid scattering media
论文作者
论文摘要
医学成像中质量恶化的一种天然来源，尤其是在我们的情况下，光学相干断层扫描（OCT）是浑浊的生物学介质，其中光子不采用可预测的路径，许多散射事件会影响有效的路径长度并改变偏振光的极化。即使在高分辨率的干涉方法的情况下，这种固有的问题也会导致成像错误。为了解决这个问题并考虑了此问题的固有随机性，在过去的几十年中，提出了一些包括蒙特卡洛模拟的方法。在这种方法中，模拟将使我们对基础物理结构及其OCT成像对应进行一对一的比较。尽管它的目标是使从业者更好地了解基础结构，但它缺乏提供全面的方法来提高OCT成像的准确性和成像质量，并且只会提供有关成像方法如何流动的示例。为了减轻此问题，并展示了一种新的方法来改进医学成像而不更改任何硬件，我们介绍了由蒙特卡洛模拟组成的新管道，然后是深层神经网络。
One native source of quality deterioration in medical imaging, and especially in our case optical coherence tomography (OCT), is the turbid biological media in which photon does not take a predictable path and many scattering events would influence the effective path length and change the polarization of polarized light. This inherent problem would cause imaging errors even in the case of high resolution of interferometric methods. To address this problem and considering the inherent random nature of this problem, in the last decades some methods including Monte Carlo simulation for OCT was proposed. In this approach simulation would give us a one on one comparison of underlying physical structure and its OCT imaging counterpart. Although its goal was to give the practitioners a better understanding of underlying structure, it lacks in providing a comprehensive approach to increase the accuracy and imaging quality of OCT imaging and would only provide a set of examples on how imaging method might falter. To mitigate this problem and to demonstrate a new approach to improve the medical imaging without changing any hardware, we introduce a new pipeline consisting of Monte Carlo simulation followed by a deep neural network.
